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End-to-End Person Re-Identification for Security and Analysis 4

End-to-End Person Re-Identification for Security and Analysi...

引用

4th International Conference for Emerging Technology, INCET 2023

作者： Vishwas, R. Sathvik Rao, M.P. Siddhanth, M. Uthpal, P. Kanwal, Preet PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335750

There are millions of CCTVs deployed in public and private places which gather tremendous amounts of video data. It requires a huge amount of manpower to monitor this video data and get the required information extracted in a timely manner. To handle this problem the paper proposes a novel architecture to automate the Video surveillance system, capable of tracking each individual across a multi camera network in real time. Numerous important technologies are incorporated into the Video surveillance system, such as person detection, person re-identification and person *** effectiveness of person re-identification has been greatly improved by researchers, but majority of these methodologies use manually annotated bounding boxes for person detection and only concentrate on matching between query image and cropped gallery images with no regard to person tracking. The paper proposes the following pipeline of processes to build an end-to-end video surveillance system.•Collecting CCTV footage from a network of cameras•Person Detection using YOLOv4•Person Re-Identification using Locally Aware Transformer model for feature extraction•Person Tracking using Deep - SORT algorithm•Logs generation for further analysis and insightsThe Person Re-ID model trained using the aforementioned algorithms achieved an average Rank 1 accuracy of 81% when tested using the custom generated datasets. The resulting product is an End-to-End Person Re-ID Framework deployed as a Web portal which tracks persons across cameras and generates human readable logs for analysis. © 2023 IEEE.

关键词： Feature extraction

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Diabetes Mellitus Diagnosis using Optical Ring Resonators 18

Diabetes Mellitus Diagnosis using Optical Ring Resonators

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Prajwal, Manush Jesy Janet Kumari, J. Uppu, Maanas Mitrahass Chirchi, Vanajaroselin Vishalatchi, S. Darshan, D.N. The Oxford College of Engineering Dept. Computer Science and Engineering Bangalore India The Oxford College of Engineering Dept. of Isc Bangalore India Kalasalingam Academy of Research and Education Dept. of Ece Krishnankoil India

ISBN: (纸本)9789380544519

The research shows the use of optical ring resonators (ORRs) as a promising non-invasive tool for diagnosing diabetes mellitus, potentially replacing invasive procedures. By accurately measuring glucose levels in biological samples through resonance frequency shifts closely linked to varying glucose concentrations, ORRs point to innovative and precise glucose measurement for early diabetes detection. This advancement could transform disease monitoring by reducing patient discomfort from current invasive techniques. The study develops a highly sensitive, non-invasive method for detecting diabetes by analyzing subtle optical signal changes related to glucose levels in urine is around 165-180 mg/dl (normal) and resonant frequency output is around 36.026459 but in case of blood resonant frequency output is around0.348478amusing advanced optical ring resonator technology. The methodology involves designing optical ring resonators capable of detecting minute refractive index variations connected to glucose concentrations. Major findings demonstrate the precision of diagnosing diabetes, showcasing the potential for a rapid and reliable diagnostic tool. Interpretations emphasize the innovative approach's ability to significantly improve early detection, enabling timely intervention and enhanced diabetes management for better healthcare outcomes and quality of life for individuals with diabetes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： diabetes diagnosis diabetes mellitus non-invasive method optical signal ring resonator

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Tagging Speech for Words in Low Resourced Monolingual Contexts of Sanskrit Shlokas

Tagging Speech for Words in Low Resourced Monolingual Contex...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Hegde, Thanmayi S. Vindhya, V. Badri Prasad, V.R. Dept. Computer Science and Engineering PES University Bengaluru India

ISBN: (纸本)9798350325188

Natural Language Processing is widely used in Machine translation which helps in replacing many tedious and difficult tasks and producing qualitative and efficient results. Sanskrit is one of the primitive languages of India. It is also the oldest, purest and most systematic language in the world. It is a magical language which helps in generating different words from a single word by prefixing and suffixing the word with different helper words to same root word. Shloka in Sanskrit are phrases or words that represent a poem or a hymn. The famous texts written entirely in shlokas are the "Ramayana" and "Mahabharata." This paper aims to generate the POS tags for each word in Sanskrit Shlokas using various machine learning and deep learning algorithms thus helping in understanding the actual verbal beauty of Sanskrit Shlokas in widely spoken language English. The proposed work implements Parts of Speech Tagging for words in Sanskrit Shlokas using Conditional Random Field, Gaussian NB and Bi-directional LSTM models. Of the implemented approaches, Bi-directional LSTM gives the best result. © 2023 IEEE.

关键词： Bidirectional Long Short-Term Memory Networks Character Embeddings Conditional Random Fields Corpus building for Sanskrit Data Preprocessing Deep learning Gaussian Navie Bayes Machine translation systems Natural Language Translation Parts of speech tagging (POS)

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CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector in MIMO-enabled UAV Networks 35

CNN Autoencoder Resizer: A Power-Efficient LoS/NLoS Detector...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Akhtarshenas, Azim Ayoobi, Navid Lopez-Perez, David Toosi, Ramin Amoozadeh, Matin Universitat Politècnica de València Dept. of Telecommunication Engineering Valencia Spain University of Houston Dept. of Computer Science Houston United States University of Tehran Dept. of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350362244

Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital nonterrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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On-chain Off-chain Blockchain Model for IoT using IPFS 27

On-chain Off-chain Blockchain Model for IoT using IPFS

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27th International Conference on Advanced Computing and Communications, ADCOM 2022

作者： Khobragade, Pooja Kumar, Ashok Department of Computer Science and Engineering NIT Rourkela India Turuk Dept. Computer Science and Engineering NIT Rourkela India

ISBN: (纸本)9781839539107

Blockchain has become the most revolutionary and trending technology since 2008 with the development of Bitcoin. Besides bitcoin, blockchain can be applied in different sectors, such as finance, healthcare, IoT, security, etc. Blockchain and IoT are two new emerging technologies. IoT has become an integral part of human digital life with the exponential growth of digitally connected devices to the internet. With the development of connected devices, data communication has also increased. Till now, IoT data management has been based on a centralized server. Blockchain technology can protect IoT devices against malicious attacks and single-point failure. This study analyzes the integration of blockchain technology with IoT and also discusses the advantages, challenges in integration, and associated cyber security threats. Also, it provides an architecture for blockchain-enabled secure IoT devices with the Interplanetary File System(IPFS). © ADCOM 2022. All rights *** right reserved.

关键词： Smart contract

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A Study of Transliteration Approaches 4

A Study of Transliteration Approaches

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chaudhary, Anu Shekhar, Shashi GLA University Dept. of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350306118

In the present era, text generated by users is becoming increasingly popular on the Internet. People rely on search engines for their information. The transliterated text is one of the major concerns to search the relevant information for a user query. There is a corresponding effort to handle transliterated text in order to take advantage of this huge knowledge source. In this paper we review the most recent findings in the domain of transliteration. Firstly, start with an introduction to transliteration and its challenges, then outline several transliteration methods and discuss new research efforts in information search for transliterated text. In this study, the authors present various approaches/techniques, algorithms, and evaluation approaches of previous studies, identify some important issues in machine transliteration approaches/models, and provide future guidance for researchers in this field. © 2023 IEEE.

关键词： Search engines

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Performance and Evaluation of Classification Algorithms Using Machine Learning with Comparative Study for Heart Disease Prediction

Performance and Evaluation of Classification Algorithms Usin...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Hossain, Md. Manik Ashraful Alam, Md. Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

The leading cause of unseasonable death worldwide is heart complaints. Day by day the causes of heart disease are increasing at a rapid-fire rate and it's veritably important and concerning to prognosticate any such disease beforehand. Predicting how illness will affect a person is a delicate challenge. Machine learning is being applied in different fields around the world. In the healthcare sector, there is no exception. Data classification models and machine learning algorithms stoutly introduce diagnostic guidelines and enable experts to increase the effectiveness of the diagnostic process. The body's remaining organs are given advanced precedence over the nucleus. It provides the body with oxygen. The distribution of heart illnesses among medical practitioners can be estimated via data exploration. Medical facilities can examine various disorders and evaluate emerging diseases thanks to data collecting. Predicting the condition based on recent medical research has the biggest impact. To learn statistics more effectively, a variety of methods are being investigated in the scientific community. The renovation has had a big impact on the metropolitan community's way of life in addition to improving it. In this situation, it's crucial to offer a comprehensive tool that will enable medical professionals to foresee the sickness. Different machine learning applications suggest varying prediction precision. It should be analyzed with Logistic Regression, KNN, Decision tree, Random Forest, SVM, Gaussian NB, Ada Boost Classifier Gradient Boosting Classifier, Quadratic Discriminant Analysis, and MLP Classifier with comparative mean of three data sets. It will be better to investigate the accuracy of prediction from the most concerning algorithms of Machine learning with recall and f-score of the heart data and present it in the table with visual representation. This underpinning research has shown that multilayer perceptron with cross-validation has surpassed all other alg

关键词： Heart

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Effective Feature Selection and Soft Voting Classifier based Diabetes Detection Using Machine Learning Approaches

Effective Feature Selection and Soft Voting Classifier based...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Suchi, Tania Alam Rabbi, Md. Anayt Layek, Md. Abu Jagannath University Dept. of Computer Science and Engineering Dhaka Bangladesh Jagannath University Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

An unnecessarily increased blood glucose level is a symptom of the metabolic disorder diabetes mellitus (DM). Early diabetes detection is reduce the risk factor of kidney disease, heart failure, liver cirrhosis, it might be hazardous. Our motivation is to developed a machine learning models to forecast diabetes in the future. We applied efficient data cleaning, label encoding, normalization and pre-processing methods for better models accuracy. Prediction with synthetic minority over-sampling (SMOTE) implies the equilibrium of an unstable dataset. Different feature selection techniques such as Chi-square test, Information gain attribute evaluator, Extra trees classifier, SelectKbest, and correlation-based approach to finding the key variables. Machine learning algorithms including Random Forest(RF), K-nearest neighbor(KNN), Naive Bayes (NB), Support Vector Machine (SVM), Gradient Boosting (GB), Decision Tree (DT), and Logistic Regression (LR), have been applied in the initial phase. Random Forest accuracy of 95.03% after normalizing the dataset with synthetic minority over-sampling (SMOTE) techniques. We using a voting classifier for ensemble all machine learning models in the second phase. The models achieved soft voting highest accuracy 96.69%. The findings of our models comparing with some current research indicate that it can offer greater accuracy. Now, we can accurately forecast diabetes. © 2023 IEEE.

关键词： Feature Selection

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Automatically Suggesting Suitable Crop from Soil Samples Using Optimized Neural Networks 3

Automatically Suggesting Suitable Crop from Soil Samples Usi...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Pasha, Syed Nawaz Ramesh, D. Mohmmad, Sallauddin Deepak, Gujjula School of Computer Science and Artificial Intelligence Sr University Warangal India Kamala Institute of Technology and Science Dept. of Computer Science and Engineering Huzurabad India

ISBN: (纸本)9798350381931

A soil-centric crop recommendation system recommends suitable crops utilizing vital soil attributes as input factors. This sophisticated system incorporates parameters like Nitrogen (N), Phosphorus (P), Potassium (K) levels, temperature, humidity, pH, and rainfall to suggest ideal crops for cultivation. The dataset comprises diverse crops and their specific requirements for each input parameter. We implemented an optimized neural network to provide precise crop recommendations based on the provided input values. By analyzing this data, the system provides farmers with the information necessary to make well-informed decisions regarding their choice of crops, enhancing the potential for optimal yields. Our model performed well on training and testing data with an accuracy of 0.94. These results demonstrate its effectiveness and potential as a valuable tool for agricultural decision-making. This system offers farmers tailored recommendations based on soil properties, climatic conditions, and other environmental factors. © 2024 IEEE.

关键词： Crops

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Machine Learning Advances in Diagnosing Attention Deficit and Hyperactivity Disorder 14

Machine Learning Advances in Diagnosing Attention Deficit an...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Deepika Sharma, Meghna Arora, Shaveta The Northcap University Dept. of Computer Science and Engineering Gurugram India

Attention deficit and hyperactivity disorder (ADHD) is worldwide prevalent neurobehavioral ailment affecting kids and adolescents. ADHD is mainly characterized by behavioral and mental abnormalities with its root lying in genetics and neural impairments. Despite prescription of various diagnosis guidelines by American psychiatric association (APA), there is no standard biomarker for ADHD detection which sometimes causes misdiagnosis/overdiagnosis of the symptoms. So, there is crucial need for an accurate and reliable diagnostic standard for ADHD for timely and effective treatment. This paper reviews various machine learning and deep learning models proposed for ADHD diagnosis in recent times. It also lists different factors supporting the use of machine learning for ADHD detection, brain imaging modalities used, novel execution strategies along with the shortcomings of these methods. The conclusion part talks about future scope of machine learning in ADHD diagnosis. © Grenze Scientific Society, 2023.

关键词： Brain mapping

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